This MLS-C01 practice question tests your understanding of modeling. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is using SageMaker to deploy a model for real-time inference. The model requires GPU for low latency. Which THREE configurations should the company consider for high availability and cost optimization? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a multi-model endpoint to share GPU instances among multiple models.
Option B is correct because a multi-model endpoint allows multiple models to be hosted on the same GPU-backed instance, sharing the GPU resources and reducing idle time. This improves cost efficiency by maximizing GPU utilization while still providing low-latency inference for each model. It is a recommended pattern for serving many models with GPU requirements without provisioning separate endpoints.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use Spot instances for the endpoint.
Why it's wrong here
Spot instances can be interrupted, affecting availability.
✓
Use a multi-model endpoint to share GPU instances among multiple models.
Why this is correct
Increases GPU utilization and reduces cost.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use SageMaker Batch Transform for inference.
Why it's wrong here
Batch Transform is not for real-time inference.
✓
Use multiple production variants with different instance types.
Why this is correct
Allows fallback if one instance type is unavailable.
Related concept
Read the scenario before looking for a memorised answer.
✓
Enable automatic scaling based on invocation count.
Why this is correct
Scales instances to handle demand.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse high availability with cost optimization, incorrectly assuming Spot instances (Option A) are suitable for real-time inference despite their interruption risk, or they overlook multi-model endpoints as a GPU-sharing strategy.
Detailed technical explanation
How to think about this question
Multi-model endpoints use a shared container that loads and unloads models dynamically from Amazon S3, caching them in GPU memory to reduce cold-start latency. Under the hood, SageMaker manages model loading and eviction based on access patterns, allowing a single GPU instance to serve dozens of models efficiently. In a real-world scenario, a company with many small models (e.g., per-customer recommendation models) can reduce costs by 10x compared to deploying each model on its own GPU endpoint.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a multi-model endpoint to share GPU instances among multiple models. — Option B is correct because a multi-model endpoint allows multiple models to be hosted on the same GPU-backed instance, sharing the GPU resources and reducing idle time. This improves cost efficiency by maximizing GPU utilization while still providing low-latency inference for each model. It is a recommended pattern for serving many models with GPU requirements without provisioning separate endpoints.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
Question Discussion
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